RT Journal
A1 Ciernik, Laure
A1 Kraft, Agnieszka
A1 Barkmann, Florian
A1 Yates, Josephine
A1 Boeva, Valentina
T1 Robust and efficient annotation of cell states through gene signature scoring
JF Genome Research 
JO Genome Research 
YR 2026 
FD March 01 
VO 36 
IS 3 
SP 630 
OP 644 
DO 10.1101/gr.280926.125 
UL http://genome.cshlp.org/content/36/3/630.abstract 
AB Gene signature scoring is integral to single-cell RNA sequencing (scRNA-seq) data analysis, particularly for unsupervised cellular state annotation based on maximum signature score values. However, this application requires robust and comparable score distributions across diverse signatures and experimental conditions. Our systematic evaluation of established scoring methodologies—Seurat, SCANPY, UCell, and JASMINE—across nine healthy and cancer scRNA-seq data sets demonstrates their insufficiency in fulfilling this requirement. To address this limitation, we present Adjusted Neighborhood Scoring (ANS), a deterministic algorithm with enhanced control gene selection that significantly improves score stability and cross-signature comparability, achieving cell-state annotation accuracy comparable to supervised methods. We demonstrate the practical utility of ANS by developing and validating a gene signature to differentiate cancer-associated fibroblasts from malignant cells undergoing epithelial-to-mesenchymal transition. Overall, ANS provides a robust and reliable gene signature scoring framework, significantly improving the accuracy of score-based annotation of cell types and states in single-cell studies.